AI workflow orchestration tools comparison

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
import os

# Initialize chat model
chat = ChatOpenAI(model="gpt-4o-mini", temperature=0, api_key=os.environ["OPENAI_API_KEY"])

# Define prompt template
prompt = ChatPromptTemplate.from_template("Answer the question based on the context: {context}\nQuestion: {question}")

# Example documents and vector store
loader = TextLoader("example.txt")
docs = loader.load()
vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())

# Query vectorstore and run chain
query = "What is AI workflow orchestration?"
results = vectorstore.similarity_search(query, k=3)
context = "\n".join([doc.page_content for doc in results])

response = chat.invoke([{"role": "user", "content": prompt.format(context=context, question=query)}])
print(response.content)

from composio_openai import ComposioToolSet, Action
from openai import OpenAI
import os

# Initialize Composio toolset and OpenAI client
toolset = ComposioToolSet(api_key=os.environ["COMPOSIO_API_KEY"])
tools = toolset.get_tools(actions=[Action.GITHUB_STAR_A_REPOSITORY])
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Create chat completion with tools
response = client.chat.completions.create(
    model="gpt-4o-mini",
    tools=tools,
    messages=[{"role": "user", "content": "Star the openai/openai-python repo"}]
)

# Handle tool calls
result = toolset.handle_tool_calls(response)
print(result)